Computational Measures of Political Volatility

It is now generally accepted that politics has become more fast-moving and unstable, which has led to election surprises and even regime changes. While modern politics seems more volatile, there is little systematic evidence to support this. In this project, we seek to address this gap with data reported over the last fifty years using public opinion polls and media coverage data in the UK and Germany. These countries are good cases to study because both have experienced considerable changes in electoral behaviour and new political parties during the time period studied. We use natural language processing and information theoretic approaches to quantify the trends and changes in the volatility of public opinion before and after social media became widely used. We measure volatility in public opinion and in media coverage using the K-L divergence, also known as relative entropy, which has previously been used as a proxy for 'surprise' or 'novelty'. Our analysis suggests the content in media coverage changes more quickly than public attention as expressed in opinion polls. Understanding the dynamics of this volatility in civil society will shed light on undercurrents of public opinion that have, in recent years, burst to the surface in highly unpredictable ways, and can inform the creation of early warning signals that may aid in prediction and explanation.